Genome Canada precision medicine strategy for structured national implementation of epitope matching in renal transplantation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Advances in immunology support the understanding that precise structural epitopes on the antibody-accessible region of the HLA molecule determine antigenicity and challenge the need for identity across the full HLA molecule to minimize graft immunogenicity. Retrospective studies confirm that quantitative measurement of epitope-level mismatching between donor and recipient is an informative marker of graft rejection and survival and suggest that prospective allocation of donor organs based on this principle may improve graft survival. Here we describe the process for rigorous prospective evaluation of this hypothesis in a formal national proof-of-concept program for epitope-based matching. This encompasses broad societal consultation to engage the public, patients and providers; the development of clear allocation policies with strategies to support candidates who may be difficult to match; molecular and sequencing methods and web-based calculators enabling rapid epitope typing and recipient selection; precise immunological monitoring of the graft response; information systems permitting real-time monitoring of clinical outcomes; and assessment of health benefit and economic cost. The results of this objective evaluation can then be provided to payers and policy-makers for review, and adoption if of proven benefit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it